Utilizing Drones to Restore and Maintain Radio Communication During Search and Rescue Operations
Bibliographic record
Abstract
INTRODUCTION: The ability of rescuers to maintain contact with incident command (IC) and each other is a critical component of search and rescue (SAR) operations. When rescuers lose radio communication with operation leaders, the effectiveness of operations may be substantially affected. This often occurs owing to the limitations of standard communications equipment in difficult terrain or when victims are beyond line-of-sight. This study investigates the viability of using an aerial drone-repeater system configuration to restore and maintain radio communications between IC and deployed rescuers. METHODS: SAR operators in Southern Utah identified 10 areas where radio communication is compromised during live rescue operations. Trained SAR personnel were deployed to these areas in a mock exercise. After confirmed loss of communication, a repeater-equipped aerial drone was piloted 122 m above IC to restore communication. Once restored, communication was assessed at regular intervals for the duration of the mock deployment. RESULTS: In all 10 areas tested, communication was successfully restored. In all cases, once communication was restored, no additional loss of radio contact occurred. The time between communication loss and restoration across the 10 scenarios was 6.5±1.1 (4.4-9.3) min (mean±SD with range). CONCLUSIONS: This method of restoring radio communication among SAR personnel could drastically improve the ability to assist victims and help mitigate the risks faced by rescuers. SAR leaders should be made aware of the useful applications of drones during SAR operations, especially in instances where communication is compromised.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".